| Topic | Time |
|---|---|
| Introduction | 8:00 - 8:45 |
| Deep Learning 1 | 8:45 - 9:45 |
| Break | 9:45 - 10:00 |
| Deep Learning 2&3 | 10:00 - 12:00 |
| Lunch break | 12:00 - 13:30 |
| Deep Learning Hands on Session | 13:30 - 15:00 |
| Break | 15:00 - 15:15 |
| Big Data Cloud Platform Lecture and hands on | 15:15 - 16:00 |
| Analytical dashboard and report | 16:00 - 16:30 |
| Soft Skill and Project Cycle | 16:30 - 17:15 |
| Q&A | 17:15 - 17:30 |
Data science is the discipline of making data useful. Ok…so what is it?
“I know it when I see it. (Potter Stewart)”
Engineering: the process of making everything else possible
Analysis: the process of turning raw information into insights in a fast way
Modeling: the process of diving deeper into the data to discover the pattern we don’t easily see
(It is a group work from https://github.com/brohrer/academic_advisory/blob/master/authors.md !)
Data environment: data storage, Kafka platform, Hadoop and Spark cluster etc.
Data management: parsing the logs, web scraping, API queries, and interrogating data streams.
Production: integrate model and analysis into the production system
Domain knowledge
Exploratory analysis
Story telling
Supervised learning
Unsupervised learning
Customized model development
The deep learning slides are based on Andrew Ng’s course: Deep Learning Specialization: Super awesome!